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1 – 10 of over 1000The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and…
Abstract
Purpose
The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.
Design/methodology/approach
A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.
Findings
The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.
Research limitations/implications
The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.
Originality/value
This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.
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Suchismita Swain, Kamalakanta Muduli, Anil Kumar and Sunil Luthra
The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships…
Abstract
Purpose
The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.
Design/methodology/approach
Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.
Findings
The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.
Practical implications
Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.
Originality/value
At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.
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Graeme Newell and Muhammad Jufri Marzuki
Healthcare property has become an important alternate property sector in recent years for many international institutional investors. The purpose of this paper is to assess the…
Abstract
Purpose
Healthcare property has become an important alternate property sector in recent years for many international institutional investors. The purpose of this paper is to assess the risk-adjusted performance, portfolio diversification benefits and performance dynamics of French healthcare property in a French property portfolio and mixed-asset portfolio over 1999–2020. French healthcare property is seen to have different performance dynamics to the traditional French property sectors of office, retail and industrial property. Drivers and risk factors for the ongoing development of the direct healthcare property sector in France are also identified, as well as the strategic property investment implications for institutional investors.
Design/methodology/approach
Using annual total returns, the risk-adjusted performance, portfolio diversification benefits and performance dynamics of French direct healthcare property over 1999–2020 are assessed. Asset allocation diagrams are used to assess the role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio. The role of specific drivers for French healthcare property performance is also assessed. Robustness checks are also done to assess the potential impact of COVID-19 on the performance of French healthcare property.
Findings
French healthcare property is shown to have different performance dynamics to the traditional French property sectors of office, retail and industrial property. French direct healthcare property delivered strong risk-adjusted returns compared to French stocks, listed healthcare and listed property over 1999–2020, only exceeded by direct property. Portfolio diversification benefits in the fuller mixed-asset portfolio context were also evident, but to a much lesser extent in a narrower property portfolio context. Importantly, this sees French direct healthcare property as strongly contributing to the French property and mixed-asset portfolios across the entire portfolio risk spectrum and validating the property industry perspective of healthcare property being low risk and providing diversification benefits in a mixed-asset portfolio. However, this was to some degree to the loss or substitution of traditional direct property exposure via this replacement effect. French direct healthcare property and listed healthcare are clearly shown to be different channels in delivering different aspects of French healthcare performance to investors. Drivers of French healthcare property performance are also shown to be both economic and healthcare-specific factors. The performance of French healthcare property is also shown to be different to that seen for healthcare property in the UK and Australia. During COVID-19, French healthcare property was able to show more resilience than French office and retail property.
Practical implications
Healthcare property is an alternate property sector that has become increasingly important in recent years. The results highlight the important role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio, with French healthcare property having different investment dynamics to the other traditional French property sectors. The strong risk-adjusted performance of French direct healthcare property compared to French stocks, listed healthcare and listed property sees French direct healthcare property contributing to the mixed-asset portfolio across the entire portfolio risk spectrum. French healthcare property’s resilience during COVID-19 was also an attractive investment feature. This is particularly important, as many institutional investors now see healthcare property as an important property sector in their overall portfolio; particularly with the ageing population dynamics in most countries and the need for effective social infrastructure. The importance of French direct healthcare property sees direct healthcare property exposure accessible to investors as an important alternate real estate sector for their portfolios going forward via both non-listed healthcare property funds and the further future establishment of more healthcare REITs to accommodate both large and small institutional investors respectively. The resilience of French healthcare property during COVID-19 is also an attractive feature for future-proofing an investor’s portfolio.
Originality/value
This paper is the first published empirical research analysis of the risk-adjusted performance, diversification benefits and performance dynamics of French direct healthcare property, and the role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio. This research enables empirically validated, more informed and practical property investment decision-making regarding the strategic role of French direct healthcare property in a portfolio; particularly where the strategic role of direct healthcare property in France is seen to be different to that in the UK and Australia via portfolio replacement effects. Clear evidence is also seen of the drivers of French healthcare property performance being strongly influenced by healthcare-specific factors, as well as economic factors.
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Salima Hamouche, Zakariya Chabani and Mohamed Dawood Shamout
The prevention of mental health issues at work represents a significant challenge for organizations. The transformation of workplaces whose future promises to be virtual or hybrid…
Abstract
Purpose
The prevention of mental health issues at work represents a significant challenge for organizations. The transformation of workplaces whose future promises to be virtual or hybrid can make the anticipation and prevention of these health issues more challenging, considering the potential distance that it may create between employees and their employers. The recent health crisis undermined individual mental health but also highlighted the importance of new technologies which greatly paved the way for the future of workplaces. This paper aims to examine these new technologies, specifically the use of blockchain technologies in organizations to predict and prevent mental health issues at work, specifically psychological distress, in times of crisis, and beyond. It addresses the main challenges and opportunities and presents research avenues as well as insights for human resource management (HRM) practitioners.
Design/methodology/approach
This paper is a viewpoint that addresses the use of blockchain technology in the prevention of employees’ mental health at work in times of crisis and beyond. Literature was used to support this viewpoint and highlight the importance of addressing mental health issues at work and preventing their occurrence in the future.
Findings
Blockchain is one of the disruptive new technologies that can be used as a strategic tool for organizations to prevent mental health issues among employees in the workplace in times of crisis, and beyond. It facilitates the collaboration between employees, their organization, healthcare and employee assistance program (EPA) providers, as well as insurance companies. In this context, a specific type of blockchain should be used to support this type of collaboration.
Practical implications
Blockchain can generate both opportunities and challenges for the prevention of mental issues at work. It can transform the future of workplaces and help organizations as well as healthcare and EPA providers to anticipate potential employees’ mental health issues in 2019. Organizations need to address their readiness to implement this new technology and the possible reluctance of their employees to use it. This paper presents insights for managers and HRM practitioners.
Originality/value
The studies that have addressed the use of blockchain in organizations to prevent employees’ mental health issues are sparse. This paper is an attempt to address this gap and examine the challenges as well as the opportunities associated with the use of this disruptive new technology that can significantly reshape the future of workplaces.
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Nidhi Raghav and Anoop Kumar Bhola
To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a…
Abstract
Purpose
To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a modern decentralized blockchain, safe and easy-to-use health-care technology application in the cloud.
Findings
On observing the graph, the convergence analysis of proposed Levy Flight-integrated moth flame optimization method at 80th iteration was 4.59%, 2.80%, 3.316%, 8.92% and 2.55% higher than the traditional models MFO, artificial bee colony (ABC), particle swarm optimization (PSO), moth search algorithm (MSA) and glow worm swarm optimization (GWSO), respectively, for Hungarian data set. Particularly, in best case scenario, the adopted method attains low cost value (5.672671) when compared to all other traditional models such as MFO (5.727314), ABC (5.711577), PSO (5.706499), MSA (5.764517) and GWSO (5.723353).
Originality/value
The proposed method achieved effective performance in terms of key sensitivity, sanitization effectiveness, restoration effectiveness, etc.
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WenFeng Qin, Yunsheng Xue, Hao Peng, Gang Li, Wang Chen, Xin Zhao, Jie Pang and Bin Zhou
The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation…
Abstract
Purpose
The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.
Design/methodology/approach
A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.
Findings
The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.
Originality/value
The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.
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Dimitrios Markopoulos, Anastasios Tsolakidis, Ioannis Triantafyllou, Georgios A. Giannakopoulos and Christos Skourlas
This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future…
Abstract
Purpose
This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).
Design/methodology/approach
Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.
Findings
Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.
Research limitations/implications
The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.
Originality/value
The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.
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Weng Marc Lim, Maria Vincenza Ciasullo, Octavio Escobar and Satish Kumar
The goal of this article is to provide an overview of healthcare entrepreneurship, both in terms of its current trends and future directions.
Abstract
Purpose
The goal of this article is to provide an overview of healthcare entrepreneurship, both in terms of its current trends and future directions.
Design/methodology/approach
The article engages in a systematic review of extant research on healthcare entrepreneurship using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) as the review protocol and bibliometrics or scientometrics analysis as the review method.
Findings
Healthcare entrepreneurship research has fared reasonably well in terms of publication productivity and impact, with diverse contributions coming from authors, institutions and countries, as well as a range of monetary and non-monetary support from funders and journals. The (eight) major themes of healthcare entrepreneurship research revolve around innovation and leadership, disruption and technology, entrepreneurship models, education and empowerment, systems and services, orientations and opportunities, choices and freedom and policy and impact.
Research limitations/implications
The article establishes healthcare entrepreneurship as a promising field of academic research and professional practice that leverages the power of entrepreneurship to advance the state of healthcare.
Originality/value
The article offers a seminal state of the art of healthcare entrepreneurship research.
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Priyanka Thakral, Dheeraj Sharma and Koustab Ghosh
Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company…
Abstract
Purpose
Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.
Design/methodology/approach
A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.
Findings
The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.
Originality/value
Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.
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Shefali Arora, Ruchi Mittal, Avinash K. Shrivastava and Shivani Bali
Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in…
Abstract
Purpose
Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.
Design/methodology/approach
The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.
Findings
By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.
Originality/value
There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.
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